class_docstr: An `EventMultiplexer` manages access to multiple `EventAccumulator`s.
  
  Each `EventAccumulator` is associated with a `run`, which is a self-contained
  TensorFlow execution. The `EventMultiplexer` provides methods for extracting
  information about events from multiple `run`s.
  
  Example usage for loading specific runs from files:
  
  ```python
  x = EventMultiplexer({'run1': 'path/to/run1', 'run2': 'path/to/run2'})
  x.Reload()
  ```
  
  Example usage for loading a directory where each subdirectory is a run
  
  ```python
  (eg:) /parent/directory/path/
        /parent/directory/path/run1/
        /parent/directory/path/run1/events.out.tfevents.1001
        /parent/directory/path/run1/events.out.tfevents.1002
  
        /parent/directory/path/run2/
        /parent/directory/path/run2/events.out.tfevents.9232
  
        /parent/directory/path/run3/
        /parent/directory/path/run3/events.out.tfevents.9232
  x = EventMultiplexer().AddRunsFromDirectory('/parent/directory/path')
  (which is equivalent to:)
  x = EventMultiplexer({'run1': '/parent/directory/path/run1', 'run2':...}
  ```
  
  If you would like to watch `/parent/directory/path`, wait for it to be created
    (if necessary) and then periodically pick up new runs, use
    `AutoloadingMultiplexer`
  @@Tensors
docstr: Return summary metadata for all time series.
  
  Returns:
    A nested dict `d` such that `d[run][tag]` is a
    `SummaryMetadata` proto for the keyed time series.
docstr_tok: ["Return","summary","metadata","for","all","time","series",".","Returns",":","A","nested","dict","`","d","`","such","that","`","d","[","run","]","[","tag","]","`","is","a","`","SummaryMetadata","`","proto","for","the","keyed","time","series","."]
code: def AllSummaryMetadata(self):
      """Return summary metadata for all time series.
  
      Returns:
        A nested dict `d` such that `d[run][tag]` is a
        `SummaryMetadata` proto for the keyed time series.
      """
      with self._accumulators_mutex:
          # To avoid nested locks, we construct a copy of the run-accumulator map
          items = list(self._accumulators.items())
      return {
          run_name: accumulator.AllSummaryMetadata()
          for run_name, accumulator in items
      }
  
imports: import os
  import queue
  import threading
  from typing import Optional
  from tensorboard.backend.event_processing import directory_watcher
  from tensorboard.backend.event_processing import plugin_event_accumulator
  from tensorboard.backend.event_processing import io_wrapper
  from tensorboard.util import tb_logging
label_desc: Use image node_id 17 for calling the EventMultiplexer obj's underlying member method code with example usage: obj.AllSummaryMetadata() and returns: unknown